Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

ICML 2015
Volume: 37, Pages: 1218 - 1226
Published: Jul 6, 2015
Abstract
Recent advances in stochastic gradient variational inference have made it possible to perform variational Bayesian inference with posterior approximations containing auxiliary random variables. This enables us to explore a new synthesis of variational inference and Monte Carlo methods where we incorporate one or more steps of MCMC into our variational approximation. By doing so we obtain a rich class of inference algorithms bridging the gap...
Paper Details
Title
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Published Date
Jul 6, 2015
Journal
Volume
37
Pages
1218 - 1226
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